Privacy preserving data mining has become an important issue in recent years due to the large amount of consumer data tracked by automated systems on the Internet. The proliferation of electronic commerce on the World Wide Web has resulted in the storage of large amounts of transactional and personal information about users. In addition, advances in hardware technology have also made it more feasible to track information about individuals from transactions in everyday life.
For example, a simple transaction such as using a credit card results in automated storage of information about user buying behavior. In many cases, users are not willing to supply such personal data unless its privacy is guaranteed. Therefore, in order to ensure effective data collection, it is important to design methods which can mine the data with a guarantee of privacy.
However, while there has been a considerable amount of focus on privacy preserving data collection and mining methods in recent years, such methods assume homogeneity in the privacy level of different entities.
Accordingly, it would be highly desirable to provide improved techniques for use in accordance with a privacy preserving data mining.